Graph distance and feature-guided multi-view clustering: A novel method for clustering urban buildings |
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| Authors: | Lin Yang Ruolin Yang Zejun Zuo Mei-Po Kwan Shunping Zhou |
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| Institution: | 1. School of Computer Science, China University of Geosciences, Wuhan, China;2. National Engineering Research Center of Geographic Information System, China University of Geosciences, Wuhan, China;3. Department of Geography and Resource Management, Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong, China |
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| Abstract: | Urban buildings are an integral component of urban space, and accurately identifying their spatial configurations and grouping them is vital for various urban applications. However, most existing building clustering methods only utilize the original spatial and nonspatial features of buildings, disregarding the potential value of complementary information from multiple perspectives. This limitation hinders their effectiveness in scenarios with intricate spatial configurations. To address this, this article proposes a novel multi-view building clustering method that captures cross-view information from spatial and nonspatial features. Drawing inspiration from both spatial proximity characteristics and nonspatial attributes, three views are established, including two spatial distance graphs (centroid distance graph and the nearest outlier distance graph) and a building attribute graph (multiple-attribute graph). The three graphs undergo iterative cross-diffusion processes to amplify similarities within each predefined graph view, culminating in their fusion into a unified graph. This fusion facilitates the comprehensive correlation and mutual enhancement of spatial and nonspatial information. Experiments were conducted using 10 real-world community-building datasets from Wuhan and Chengdu, China. The results demonstrate that our approach achieves 21.27% higher accuracy and 22.28% higher adjusted rand index in recognizing diverse complex arrangements compared to existing methods. These findings highlight the importance of leveraging complementary and consensus information across different feature dimensions for improving the performance of building clustering. |
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